KR20170136412A - System and method for providing inter-vehicle communications amongst autonomous vehicles - Google Patents

Abstract

According to an embodiment of the present invention, a vehicle identifier (ID) and vehicle information of a first autonomous vehicle are transmitted to a cloud server communicatively coupled to a plurality of autonomous vehicles via a network. A list of one or more vehicle identifiers (IDs) is received from the cloud server. The vehicle IDs identify one or more autonomous vehicles within predetermined geographic proximity of the first autonomous vehicle. The first autonomous vehicle communicates with a second autonomous vehicle selected from the list of one or more autonomous vehicles via a wireless local area network to exchange an operational status between the first autonomous vehicle and the second autonomous vehicle.

Description

[0001] SYSTEM AND METHOD FOR PROVIDING INTER-VEHICLE COMMUNICATIONS [0002] AMONGST AUTONOMOUS VEHICLES [0003]

An embodiment of the present invention relates to an unmanned driving vehicle. More particularly, embodiments of the present invention relate to providing vehicle-to-vehicle communication between unattended driving vehicles.

A vehicle that travels in an autonomous mode (e.g., unmanned or autonomous) may cause a passenger, and in particular a driver, to deviate from the responsibilities associated with some driving. When driving in the autonomous mode, the unmanned vehicle can be guided to various locations using the vehicle sensors, allowing the vehicle to travel under minimal human-computer interaction situations or in some situations without passengers. Except for the objective circumstances of the surrounding environment, the unmanned vehicle needs to detect not only the objective circumstances of the surrounding environment but also the subjective intent of other nearby vehicles so as to make an appropriate prediction and correct driving decision. For example, if a vehicle in the right lane changes its lane and attempts to occupy the space in front of the unmanned vehicle, this may not be the right time to accelerate in an unmanned vehicle, have.

Typically, a human operator acquires this type of information by observing a traffic light, through a reflector, or directly looking back. In order to exchange information between unmanned driving vehicles, communicating directly through the local network is a more effective and reliable method. However, building and maintaining this type of communication network, which is used professionally in unmanned driving vehicles, is still a challenging task.

The present invention provides a computer realization method for inter-vehicle communication between unmanned operation vehicles, a non-transitory machine-readable medium, a data processing system operating in an unmanned operation vehicle, and a computer realization method for managing the state of an unmanned operation vehicle .

According to a first aspect of the present invention, there is provided a computer realization method for vehicle-to-vehicle communication between an unmanned operation vehicle, comprising the steps of: To a cloud server communicably connected to a plurality of unmanned driving vehicles; Receiving, from the cloud server, a first list of one or more vehicle identifiers (IDs) that recognize one or more unattended operating vehicles within a predetermined geographical proximity of the first unattended vehicle; And a second unattended operating vehicle (1) in a first list selected from the one or more unmanned operating vehicles through a wireless local area network (WLAN) so that the first unmanned operating vehicle and the second unmanned operating vehicle exchange operating states. And communicating with the mobile terminal.

In a second aspect of the present invention there is provided a non-volatile machine-readable medium having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (ID) of the first unmanned driving vehicle and the vehicle information to the cloud server communicably connected to the plurality of unmanned driving vehicles through the network, and transmits, from the cloud server, A first list of one or more vehicle identifiers (IDs) that recognize one or more unmanned driving vehicles within a geographical access map, and wherein the first unmanned driving vehicle and the second unmanned driving vehicle Through the wireless local area network, a first one selected from the one or more unmanned driving vehicles And communicating with the second unattended operating vehicle of the list.

In a third aspect of the present invention, there is provided a data processing system operating in an unmanned on-board vehicle, the data processing system comprising: a processor; And a memory coupled to the processor and configured to store instructions, wherein the instructions, when executed by the processor, cause the processor to perform the steps of: transmitting a vehicle identifier (ID) of the first unattended operating vehicle and vehicle information To the cloud server communicatively coupled to the unmanned driving vehicle of the first unmanned vehicle, and to receive, from the cloud server, one or a plurality of vehicle identifiers Selected from the one or more unmanned operation vehicles via a wireless local area network to receive a first list of identification information (ID) of the first unmanned operation vehicle and to exchange operating states of the first unmanned operation vehicle and the second unmanned operation vehicle, And communicating with the second unattended operating vehicle of the list.

According to a fourth aspect of the present invention, there is provided a computer realization method for managing the state of an unmanned driving vehicle, the computer realizing method comprising: in a cloud server, a plurality of unattended operating vehicles communicably connected to the cloud server via a network Maintaining an adjacent vehicle data structure storing vehicle metadata of the vehicle; Receiving a first update request including a first vehicle identifier (ID) of the first unmanned driving vehicle and current vehicle metadata from a first driving vehicle of the unmanned driving vehicle; In response to the first update request, update the neighboring vehicle data structure to update the first node corresponding to the first unmanned driving vehicle based on the first vehicle ID of the first unmanned driving vehicle and the current vehicle metadata Traversing (tr un-driving vehicle ersing); And one or more within the predetermined geographical proximity of the first unmanned operating vehicle through the network to allow the first unmanned operating vehicle to communicate directly with any unattended operating vehicle in the list via a wireless local area network And transmitting to the first unmanned operating vehicle a list of one or more vehicle IDs that recognize the unmanned operating vehicle of the first unmanned operating vehicle.

Embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like reference numerals designate like elements.
1 is a block diagram illustrating a networked system in accordance with an embodiment of the present invention.
2 is a flowchart illustrating a process flow of an unmanned vehicle according to an embodiment of the present invention.
3 is a block diagram illustrating a network configuration according to an embodiment of the present invention.
4 is a block diagram illustrating an unmanned operation vehicle management apparatus according to an embodiment of the present invention.
5 is a block diagram illustrating an example of a data structure for storing vehicle information according to an embodiment of the present invention.
6 is a flow chart illustrating a communication process between an unmanned driving vehicle according to an embodiment of the present invention.
7 is a flow diagram illustrating a communication process between an unmanned vehicle according to an embodiment of the present invention.
8 is a block diagram illustrating a data processing system in accordance with one embodiment.

Various embodiments and aspects of the present invention will now be described with reference to the following detailed description, which will be illustrated in the accompanying drawings. The following description and drawings are illustrative of the present invention and should not be construed as limiting the invention. In the following, various specific details are set forth in order to provide a thorough understanding of the embodiments of the present invention. However, in specific instances, the description of known or conventional details will not proceed to provide a concise description of the present invention.

Reference herein to " one embodiment " or " an embodiment " means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention . Where the phrase " in one embodiment " appears in various portions of the specification, it should not necessarily denote the same embodiment.

According to one embodiment, some or all of the unmanned vehicles are communicatively coupled to a centralized server (e.g., a cloud server) via a network (e.g., a cloud network). The server periodically receives specific vehicle information such as, for example, geographic location, speed, and direction of travel from the unmanned operating vehicle. The server holds and stores the data structure or database of the vehicle information of the unmanned driving vehicle registered in the server. The data structure is also referred to as an adjacent vehicle data structure, and is updated periodically in response to update contents received from the unmanned vehicle. The data structure further stores information indicating a nearby unattended driving vehicle corresponding to a specified one of the unmanned driving vehicles. This data structure is also referred to as an adjacent or nearby vehicle data structure.

In one embodiment, in addition to updating the proximity vehicle data structure (e.g., classifying based on vehicle position, speed and direction of travel) in response to a request or update received from the vehicle, A list of one or more unattended driving vehicles in the vicinity is determined based on the vehicle information stored in the unattended driving vehicle. A list of nearby unattended operating vehicles is transmitted from the server to the unattended operating vehicle to allow the unattended operating vehicle to communicate with other unattended operating vehicles via the wireless local area network, for example, to exchange operating status of the unattended operating vehicle.

According to one aspect of the present invention, the first unmanned operation vehicle transmits the vehicle ID and the vehicle information of the first unmanned operation vehicle to the cloud server via the network. The vehicle information may include position information, speed or direction of movement of the first unmanned driving vehicle. The cloud server is connected to a plurality of unattended driving vehicles so as to be able to communicate via the network. The cloud server maintains a data structure or database to compile and store vehicle information for unmanned vehicles. Some information in the vehicle information may be received from the unmanned vehicle, and other information may be compiled and generated in the server based on previously updated information received from the unmanned vehicle. The first unmanned driving vehicle receives a list of one or more vehicle IDs that recognize one or more unmanned driving vehicles from the server. The server recognizes one or more unattended driving vehicles within a predetermined geographical proximity of the first unattended driving vehicle. Based on the vehicle ID, the first unmanned operating vehicle communicates with the second unmanned operating vehicle through the wireless local area network, thereby exchanging operating states of the second unmanned operating vehicle and the first unmanned operating vehicle.

According to another aspect of the present invention, a cloud server maintains and stores a vehicle information or vehicle data structure of a plurality of unmanned driving vehicles, and the plurality of unmanned driving vehicles are connected to a network (e.g., a cloud network, Such as the Internet, to the cloud server. The server receives the first update request from the first unattended operating vehicle. The first update request includes a first vehicle identifier (ID) of the first unmanned driving vehicle and current vehicle metadata. In response to the first update request, the server traverses the adjacent vehicle data structure and sends a first node corresponding to the first unmanned driving vehicle based on the first vehicle ID of the first unmanned driving vehicle and the current vehicle information or metadata Update. To allow the first unattended operating vehicle to communicate directly with any unattended operating vehicle in the list via the wireless local area network, the server sends a predetermined geographic access to the first unattended operating vehicle through the network to the first unattended operating vehicle Transmits a list of one or more vehicle IDs that recognize one or more unattended driving vehicles in the road.

1 is a block diagram illustrating a network configuration of an unmanned vehicle according to an embodiment of the present invention. Referring to Figure 1, in this example, a plurality of unattended operating vehicles 101A-101D are communicatively coupled to one or more centralized servers 102 via a network. Although only four unmanned operation vehicles are shown in FIG. 1, more or fewer unmanned operation vehicles can be applied. Depending on the specified situation or usable signal quality, each of the unmanned driving vehicles 101A-101D maintains a connection with the server 102 continuously or periodically. Each of the unmanned driving vehicles 101A to 101D periodically transmits specific vehicle information to the server 102 so that the specific vehicle information is stored and held in the server 102. [ The vehicle information may include location and route information of the unmanned driving vehicle, speed information and / or direction of movement. The vehicle information may further include, for example, real-time traffic or traffic information obtained by an unmanned vehicle, such as a map and point of interest (MPOI) or real-time traffic situation information. Some of this information may be obtained or compiled by the server 102.

In response to previously updated information received from the unmanned driver vehicle, the server 102 updates the database (e.g., the vehicle status / situation database or the neighbor / neighbor vehicle data structures) (not shown). In addition, the server 102 analyzes the received vehicle information to determine a list of one or more unmanned driving vehicles in a predetermined access map of the unmanned driving vehicle. Then, the vehicle ID of the unmanned driving vehicle is transmitted from the server 102 to the unattended driving vehicle being requested. Unmanned vehicles can communicate with other nearby unmanned vehicles using the nearby vehicle ID. Nearby unattended driving vehicles may form a group of unmanned driving vehicles that represent a local area network (LAN) or community. In this example, based on the vehicle information, the unmanned driving vehicles 101A to 101C can be viewed as one group, and the unmanned driving vehicles 101C to 101D can be viewed as another group. Through the vehicle ID of the neighboring unmanned driving vehicle, the same group of unmanned driving vehicles can directly exchange information (for example, driving information) by communicating directly via the wireless local area network.

2 is a flowchart illustrating a process flow of an unmanned vehicle according to an embodiment of the present invention. The process 200 may be performed by the network configuration 100 of FIG. Referring to FIG. 2, the unmanned driving vehicle 101A periodically transmits the update contents of the vehicle information to one or a plurality of centralized servers 102 via the path 201. [0040] FIG. Similarly, the unmanned driving vehicle 101B periodically transmits the update contents of the vehicle information to the server 102 via the path 202. [ As described above, the server 102 may be communicatively coupled to a plurality of unattended driving vehicles, and each of the unattended driving vehicles periodically transmits updates of its corresponding vehicle information to the server 102. [ In response to the update received from the unmanned driving vehicle (in this example, the unmanned driving vehicle 101A-101B), at block 203, the server 102 reads the vehicle information (e.g., Lt; / RTI > In addition, for each of the unmanned driving vehicles 101A to 101B, the server 102 determines a list of neighboring unattended driving vehicles corresponding to the unmanned driving vehicle, and the list is based on the vehicle information of the unmanned driving vehicle You can decide.

Subsequently, the server 102 transmits the list of one or a plurality of nearby unattended driving vehicles corresponding to the unmanned driving vehicle 101A to the unmanned driving vehicle 101A through the route 204. [ In this example, the list of nearby unattended driving vehicles may include a vehicle ID that recognizes the unmanned driving vehicle 101B. The server 102 transmits a list of one or a plurality of nearby unattended driving vehicles corresponding to the unmanned driving vehicle 101B to the unmanned driving vehicle 101B via the path 205. [ Similarly, in this example, the list of nearby unattended driving vehicles may include a vehicle ID that recognizes the unmanned driving vehicle 101A. In blocks 206-207, the unmanned driving vehicles 101A-101B may update local vehicle information, e.g., in the local persistent storage or local memory. Subsequently, the unmanned driving vehicles 101A to 102B communicate with each other using the matched vehicle ID via the path 208, for example, exchange the running states of the unmanned driving vehicles 101A to 101B.

3 is a block diagram illustrating a network configuration according to an embodiment of the present invention. The network configuration 300 may represent at least a portion of the network configuration 100 of FIG. 3, the network configuration 300 includes an unattended operating vehicle 101 that is communicatively coupled to the centralized server 102 via a network 103. As shown in FIG. Although only one unmanned driving vehicle is shown, a plurality of unmanned driving vehicles may be connected to the server 102 via the network 103 and managed thereby. For example, the unmanned driving vehicle 101 may display any one of the unmanned driving vehicles 101A to 101D as shown in Fig. The network 103 may be any type of network and may be, for example, a local area network (LAN), such as the Internet, a cellular network, a wide area network (WAN) It can be wireless. The server 102 may be any type of server or server cluster, such as a web or cloud server, an application server, a backend server, or a combination thereof.

In one embodiment, the unmanned driving vehicle 101 includes, but is not limited to, a sensor system 115 and a data processing system 110. The sensor system 115 includes various selection or sensing devices needed to guide the unmanned driving vehicle 101 to various lanes and places. For example, the sensor system 115 may include one or more cameras, microphones, a GPS, an internal measurement unit (IMU), a radar system and / or a light sensing and measurement (LIDAR) system . The data processing system 110 is coupled to the sensor system 115 such that it is communicable, for example, via a bus, an interconnect, or a network. The data processing system 110 is operable to process any data received from the sensor system 115 and to manage or control the sensor system 115 and to communicate with the server 102 via the network 103 have. The data processing system 110 may be a dedicated computer that processes the information received from the sensor system 115 and configures the software and hardware necessary to control and operate the unmanned vehicle 101.

In one embodiment, the data processing system 110 includes a trip module 121, a cloud interface module 122, a content presentation module 123, a connection management device 124 and a data storage device 125, It is not limited. The modules 121 to 124 may be implemented in software, hardware, or a combination thereof. For example, the modules 121 to 124 may be loaded into system memory and executed by one or more processors of the data processing system 110. The trip module 121 manages any data related to the trip of the user boarding the unmanned operation vehicle 101. [ The user can designate the start position and the destination of the trip by logging in, for example, through the user interface. The trip module 121 communicates with other members of the unmanned operating vehicle 101 to obtain data related to the trip. For example, the trip module 121 may obtain location and route information from the location server 106 and the map and POI (MPOI) server 105. The location server 106 provides location services and the MPOI server 105 provides POIs with map services and some locations. While the unmanned driving vehicle 101 travels along the route, the trip module 121 may acquire more real-time traffic conditions or traffic information from the traffic information system or the server (TIS) It should be noted that the servers 104-106 may be operated by a third party entity. Optionally, the functions of the servers 104 - 106 may be integrated with the server 102, where the unmanned vehicle 101 obtains the same information from the server 102.

In one embodiment, the content presentation module 123 performs a search in the content database of the data storage device 125 based on location and route information, MPOI information, and / or real-time traffic information, Recognizes a content item (e.g., a movie, special content or sponsored content, e.g., Ad) suitable for presentation. The user can recognize the selected content based on the user profile of the user who boarded the unmanned driving vehicle 101. [ And may be arranged for an item of content according to a predetermined sorting algorithm or sorting module. For example, the user profile can be sorted for the item of content based on the user information obtained from the user's profile, and the user profile can be stored in the data storage device 125. The user information may include a user preference associated with the user, a previous behavior, or a history log. Then, an item of content is selected based on the sorting. For example, as displayed on a display device, the selected content item is rendered through the content presentation module 123 and is rendered to the user.

The data storage device 125 may be maintained in a persistence storage device (e.g., a non-persistent storage device, such as a hard disk), which stores various data and may be associated with a content database, user profile, Information (e.g., location and route information, POI information). The data stored in the data storage device 125 may be obtained from various data sources such as, for example, the server 102, the traffic information server 104, the map and POI server 105 and the location server 106. For example, the content data and the user profile may be provided and may be cached at high speed by a content database and a user profile (not shown) in the data storage device 130 stored in the server 102. Data processing system 110 further includes other elements such as one or more processors (e.g., a central processing unit or CPU), system memory or communication interface (e.g., wireless communication interface), and the like.

In one embodiment, the data processing system 110 further comprises a connection management device 124 and a cloud interface module 122. The cloud interface module 122 is responsible for communication between the cloud server 102 and the unmanned vehicle (AV) management device 150. In other words, the unmanned operation vehicle management device 150 is configured to communicate with and manage a plurality of unattended operation vehicles via the network 103. [ The connection management device 124 is configured to communicate with and maintain a connection with other nearby unattended operating vehicles.

In one embodiment, the trip module 121 communicates with, for example, the sensor system 115 and the servers 104-106 to obtain current vehicle information of the unmanned operation vehicle 101. [ As described above, the vehicle information includes, but is not limited to, location and route information, speed, direction of travel, or current traffic conditions. The cloud interface module 122 is configured to update the vehicle information of the unmanned driving vehicle 101 stored in the unmanned driving vehicle condition database 131 and / or the adjacent unmanned driving vehicle information 132 stored in the data storage device 130, Receives vehicle information from module 121 and sends an update request to server 102. The request may include a vehicle ID that recognizes the unmanned driving vehicle 101 and at least a portion of the vehicle information obtained from the trip module 121. [ In response to the update request, the unmanned vehicle management apparatus 150 updates the current vehicle information and / or the adjacent unattended driving vehicle information 132 of the unmanned driving vehicle 101 in the unmanned driving vehicle state database 131. [

In addition, the unmanned driver's vehicle management device 150 determines one or more unmanned driving vehicles in a predetermined geographical proximity map corresponding to the unmanned driving vehicle 101 based on the adjacent unattended driving vehicle information 132, Based on the vehicle information of the unmanned driving vehicle stored in the unmanned driving vehicle state database 131, compilation can be performed on the adjacent unmanned driving vehicle information 132. [ It should be noted that the unmanned driving vehicle condition database 131 and the adjacent unmanned driving vehicle information 132 may be stored in a single database or a single data structure (e.g., a k-dimensional (k-d) data structure). The unmanned vehicle management apparatus 150 transmits a list of vehicle IDs for recognizing a nearby unattended operating vehicle to the unmanned operation vehicle 101 through the network 103. The vehicle ID list is received by the cloud interface module 122 And may be locally cached at high speed or cached in the data storage device 125. The connection management device 124 of the unmanned driving vehicle 101 can then communicate with any one or more of the nearby unattended driving vehicles. If the neighboring unmanned driving vehicle is within a predetermined geographical proximity (which may be indicated by neighboring unmanned driving vehicle information received from the server 102), the connection management device 124 transmits the connection to the nearby unattended driving vehicle .

In response to each update request from the unmanned driving vehicle, it may transmit the adjacent unattended driving vehicle information from the server 102. Optionally, the unmanned driving vehicle may transmit a specific request for the adjacent unmanned driving vehicle information at any time. For example, when the unmanned driving vehicle attempts to change the lane, the unmanned driving vehicle may request the adjacent unmanned driving vehicle to inform the adjacent unmanned driving vehicle information that it is attempting to change the lane. In another example, when a user of one unmanned driving vehicle attempts to communicate with another user of a neighboring unmanned driving vehicle, the user may select the unattended driving vehicle information to communicate with other users of the other unmanned driving vehicle You can make a request for.

4 is a block diagram illustrating an unmanned operation vehicle management apparatus according to an embodiment of the present invention. 4, the unmanned vehicle management apparatus 150 includes an unmanned driving vehicle speed determination module 201, an unmanned driving vehicle orientation determination module 202, an unmanned driving vehicle information updating module 203, Module 204, but is not limited thereto. The unmanned driving vehicle speed determination module is configured to periodically calculate the moving speed of the unmanned driving vehicle based on the vehicle information (e.g., position information) received from the unmanned driving vehicle. Similarly, the unmanned driving vehicle direction determination module is configured to determine a moving direction of the unmanned driving vehicle based on the vehicle information of the unmanned driving vehicle. The unmanned driving vehicle speed determination module 201 and the unmanned driving vehicle direction determination module 202 may be selectable, wherein the speed and the moving direction can be received from the unmanned operation vehicle.

The unmanned driving vehicle information updating module 203 is configured to update the unmanned driving vehicle state database 131 based on the vehicle information received from the unmanned driving vehicle. The adjacent unmanned driving vehicle determination module 204 is configured to determine one or more adjacent unattended driving vehicles corresponding to each unmanned driving vehicle or a specific unmanned driving vehicle and to update the unmanned driving vehicle adjacent information 132. [ In one embodiment, the unmanned driving vehicle proximity information 132 may be stored in the k-d tree. The k-d tree includes a plurality of nodes arranged in a hierarchical structure. Each node represents one of the unattended driving vehicles communicably connected to the server. Each node stores or quotes vehicle information corresponding to an unmanned driving vehicle, such as the information shown in Fig. 5, for example.

The k-d tree is configured to organize the spatial partition data structure of the points in the k-dimensional space. The k-d tree is a useful data structure for keyword searches (e.g., range searching and nearest neighbor searching) for applications, e.g., multidimensional searches. The k-d tree is a special case of a binary space partition tree. The nearest neighbors (NN) search algorithm is to find the closest point to the given input point in the tree. This search can be effectively accomplished by quickly removing most of the search space through the use of tree properties.

When receiving the vehicle information update content from the unmanned driver vehicle, the unmanned driver vehicle information update module 203 updates the unmanned driver vehicle state database 131 and / or the unmanned driver vehicle state database 131 based on the vehicle ID to locate an entry corresponding to the unmanned driver vehicle. When the search is executed in the adjacent unmanned driving vehicle information 132, a new entry or node is added under the condition that the new unmanned driving vehicle and the server are connected, or the existing entry or node Lt; / RTI > The adjacent unmanned driving vehicle determination module 204 determines to request a plurality of unattended driving vehicles within a predetermined geographical proximity of the unmanned driving vehicle. For example, the neighboring unattended driving vehicle determination module 204 performs a nearest neighbor search among adjacent unattended driving vehicle information 132 (e.g., k-d tree) to recognize a nearby unattended driving vehicle. Subsequently, the recognized unattended driving vehicle is transmitted again to the requesting unmanned driving vehicle. The quantity of nearby unattended driving vehicles may be determined by the server or the requesting unattended driving vehicle, or may be requested by the user who rides the vehicle. Similarly, the geographic proximity that restricts nearby unattended driving vehicles may be determined by the server or the requesting unattended driving vehicle, or may be designated by the user who rides in it.

In one embodiment, regardless of whether the unmanned driver vehicle already has existing local communications or needs to establish new local communications, each unmanned driver vehicle periodically needs to transmit its current location information to the cloud server Lt; / RTI > For example, alternative locations based on conventional GPS readings may be based on additional information obtained from other sources (e.g. location and route servers, maps and point of interest servers and / or real-time traffic information servers, etc.) May be sufficiently good to derive or determine the precise geographic location of the vehicle.

When a vehicle information update from each unattended operating vehicle in the system is received, the cloud server executes a majority of computing or coordination. This operation is intended to allow each vehicle to compute a current " neighbor " vehicle list. Here, the term " near " is defined in the upper and lower statements of the unmanned operation. In addition to the distance measure, other relevant factors such as relative position and direction of movement may be considered. For example, a " neighborhood " list may include vehicles at relatively far distances ahead, and may not include vehicles at relatively relatively close distances at the rear. In addition, vehicles traveling in the opposite direction in the same fork lane are relatively less affected. This hypothesis is similar to the situation in which a human driver forms an observation list of lanes of interest.

For an algorithm to compute a list of adjacent vehicles of all the vehicles in the system, a calibrated k-d tree containing vehicle specific metadata may be used. In one embodiment, dimension k comprises at least two dimensions that are used corresponding to latitude and longitude. Elevation above sea level may be included as a third dimension to account for multi-highway intersection or parking structure in metropolitan areas. For example, the supplemental metadata in the driving direction are integrated into the tree node to aid in guidance or filtering in the search process as described above. By using the k-d tree as a valid index data structure, the cloud server can realize the nearest neighbor search within the log (n) time, and can effectively maintain the data structure. Thus, by recording a large number of unmanned driving vehicles in a high-dimensional search tree, it is possible to prevent large-scale processing flows that manage multiple data structures of this type, in which data nodes are constantly migrating between them .

According to another embodiment, the list of the nearby unmanned driving vehicles is then returned to each unattended driving vehicle registered from the cloud server. It should be noted that this is a list of vehicles, but the cloud server does not understand the state of the current communication list of each vehicle. In this way, for example, in situations where a connection is broken or a member is transformed, the system should block the connection and be more stable. After receiving a list of nearby vehicles that appear in the form of network identifiers (e.g., IP or domain names) from the cloud server, there are two members in each vehicle for processing such a list.

One member (e.g., connection management device 124) is for establishing a new connection to a newly discovered nearby vehicle, and is not connected to a nearby vehicle, for example, Terminate the connection to a vehicle that is not in the current list. It should be noted that this connection is a direct and highly sensitive peer-to-peer connection bypassing the cloud layer. It should also be noted that the connections may be based on the global Internet or may be connected without being based on the global Internet. The connection type options include peer-to-peer connection via WiFi, WiFi direct and Bluetooth. Other components (e.g., cloud interface module 122) may be APIs associated with synchronization problems, services, and other forms of unattended operation that are used for all current active connections.

5 is a block diagram illustrating an example of a data structure for storing vehicle information according to an embodiment of the present invention. The data structure 500 may display the unmanned driving vehicle condition database 131 and / or the adjacent unattended driving vehicle information 132 of FIG. Referring to Figure 5, in this example, the data structure 500 is shown as a table. However, the data structure 500 may be implemented in various formats, such as a database. In one embodiment, the unmanned driving vehicle condition table 500 includes a plurality of entries, and each entry is recognized by the vehicle ID 501. The vehicle ID 501 may be a network address (e.g., Internet Protocol or IP address, media access control, or MAC address) associated with the vehicle. Each entry corresponds to one of the unattended driving vehicles connected to the server managing the current unattended driving vehicle. Each entry may store location information indicated by latitude 502, longitude 503, and / or elevation 504, which stores vehicle information associated with a particular unattended driving vehicle. In addition, the vehicle information may further include a speed 505 and a moving direction 506. [

The information 502-506 may be received from the unmanned vehicle. Optionally, some of the information may be determined at the server based on the alternative location information of the unmanned vehicle. For example, an unmanned driving vehicle may transmit information of an alternative GPS device related to an unmanned driving vehicle to a server. The server communicates with the location service server based on the alternate GPS information to determine more accurate location information (e.g., latitude 502, longitude 503, and / or elevation height 504). The server may determine the speed 505 and the direction of movement 506 of the unmanned driving vehicle, for example, by comparing the current location information with the previous location information.

6 is a flow chart illustrating a communication process between an unmanned driving vehicle according to an embodiment of the present invention. Process 600 may be performed by processing logic, which may include software, hardware, or a combination thereof. For example, the process 600 may be executed by the data processing system 110 of FIG. Referring to FIG. 6, at block 601, processing logic obtains vehicle information of the first unmanned operating vehicle, such as location information (e.g., latitude, longitude, and / or elevation above sea level). The processing logic may obtain this type information from its sensor system (e.g., a GPS navigation system). At block 602, processing logic sends vehicle information including the vehicle ID of the first unattended operating vehicle to a centralized server (e.g., a cloud server) via a network (e.g., a cloud network).

At block 603, the processing logic receives a list of one or more vehicle IDs that recognize one or more unattended driving vehicles from the server. The unattended driving vehicle in the list is within a predetermined geographic approach to the first unmanned driving vehicle. The server recognizes this unmanned driving vehicle based on vehicle information provided by the first unmanned driving vehicle and other information obtained from other sources. At block 604, the processing logic recognizes a second unattended operating vehicle in the list (e.g., unattended operating vehicle, possibly driven by the first unattended operating vehicle). At block 605, the first unattended operating vehicle communicates with the second unattended operating vehicle using the vehicle ID (e.g., IP address) of the second unattended operating vehicle via the wireless local area network. For example, when the first unmanned driving vehicle changes its moving direction and attempts to enter into the driving lane of the second unmanned driving vehicle, the first unmanned driving vehicle is moved to the second unmanned driving vehicle to avoid the possibility of being destroyed or hit. You may want to notify. Alternatively, a user aboard the first unmanned driving vehicle may attempt to chat with or play a game with the user aboard the second unmanned driving vehicle.

7 is a flow diagram illustrating a communication process between an unmanned vehicle according to an embodiment of the present invention. Process 700 may be executed by processing logic, which may include software, hardware, or a combination thereof. For example, the process 700 may be executed by the server 102 of FIG. Referring to FIG. 7, at block 701, processing logic receives vehicle information from a plurality of unattended operating vehicles communicatively coupled to a server via a network. The vehicle information includes at least positional information (e.g., latitude, longitude, and altitude above sea level) of the unmanned driving vehicle, speed, or moving direction of the unmanned driving vehicle. At block 702, the processing logic updates the unattended driving vehicle condition database and / or the adjacent unattended driving vehicle data structure (e.g., the k-d tree). The update operation may further classify the adjacent unattended driving vehicle data structure based on the vehicle information, wherein the node or entry representing the adjacent unattended driving vehicle is stored adjacent or adjacent to each other.

Then, at block 703, a request is received from the first unattended operating vehicle to recognize the unattended operating vehicle proximate to the first unattended operating vehicle. The request may be part of an update request for vehicle information received from the first unmanned operating vehicle. Optionally, the request may be, for example, a particular request or a sole request received from a first unmanned operating vehicle in response to a user action or an operation in which the passenger of the first unmanned operating vehicle changes the direction of travel. In response to the request, at block 704, the processing logic proceeds to search among the adjacent unattended driving vehicle information data structures based on the vehicle ID of the first unmanned driving vehicle to recognize one or more unmanned driving vehicles. The recognized unmanned driving vehicle is currently located within a predetermined geographical proximity to the first unmanned driving vehicle. For example, the processing logic invokes a neighbor search algorithm in the k-d tree that displays the adjacent unmanned driving vehicle information data structure to recognize nearby unattended driving vehicles. At block 705, the processing logic sends a list of vehicle IDs (e.g., IP addresses) identifying the nearby unattended operating vehicle to the first unattended operating vehicle to allow communication with any one of the nearby unattended operating vehicles To the unmanned driving vehicle.

The unmanned driving vehicle described above refers to a vehicle that can be set to an autonomous mode in which the vehicle moves in an environment with little or no input from the driver. Such an unmanned operating vehicle may include a sensor system having one or more sensors configured to detect information about the environment in which the vehicle is operating. The vehicle and its associated control device move in the environment using the detected information. The sensor system may include one or more cameras, microphones, a Global Positioning System (GPS), an inertial gauge (IMU), a radar system and / or an optical detection and distance measurement (LIDAR) system.

The GPS system can estimate the geographical location of the unmanned driving vehicle. The GPS system may include a transceiver operable to provide information regarding the location of the unmanned operating vehicle. The IMU unit can detect changes in the position and orientation of the unmanned driving vehicle based on the inertia acceleration. The radar unit may represent a system for sensing an object in a local environment of an unmanned vehicle using a wireless signal. In some embodiments, in addition to sensing an object, the radar unit may additionally sense the speed and / or direction of travel of the object. The LIDAR unit can detect objects in the environment where the unmanned vehicle is located by using a laser. The LIDAR unit may include one or more laser sources, laser scanners and one or more detectors among other system members. The camera may include one or more devices for capturing images of the environment around the unmanned driving vehicle. The camera may be a still camera or a video camera. The camera can be mechanically moved, for example, by rotating and / or bouncing the platform on which the camera is installed. The microphone can be configured to capture sound from the environment of the unmanned driving vehicle.

The unmanned driving vehicle may further include a computer vision system for processing and analyzing images captured by one or more cameras to recognize objects and / or features in the environment of the unmanned driving vehicle. The object may include a traffic signal, a traffic lane, other vehicles, passers-by and / or obstacles, and the like. Computer vision systems can utilize object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, a computer vision system may proceed with operations such as generating a map for an environment, tracking an object, estimating the speed of an object, and so on.

The unmanned driving vehicle may further include a navigation system for determining the driving route of the unmanned driving vehicle. For example, the navigation system can determine a sequence of speeds and travel directions that allow unmanned driving vehicles to travel in the following path. That is, this path allows the unmanned vehicle to substantially avoid obstacles that are sensed while marching towards the final destination along a path generally based on the roadway. The destination may be set according to user input by a user interface. The navigation system can dynamically update the driving route while the unmanned driving vehicle is running. The navigation system may merge the data from the GPS system and one or more maps to determine the travel path of the unmanned vehicle.

The unmanned driving vehicle may further include a collision avoidance system for recognizing, evaluating, avoiding or otherwise avoiding potential obstacles in the environment of the unmanned driving vehicle. For example, the collision avoidance system can realize a change in the navigation of the unmanned vehicle by operating one or a plurality of subsystems of the control system, performing a turning operation, a turning operation, a braking operation, and the like. The collision avoidance system can automatically determine an obstacle avoidance operation that can be realized based on surrounding traffic patterns, road conditions, and the like. The collision avoidance system may be configured so that the other sensor system does not proceed to change the direction when the unmanned vehicle detects a vehicle, a construction obstacle, or the like in an area adjacent to the area where the unmanned driving vehicle will change direction. The collision avoidance system is not only automatically available, but also can be selected to maximize the safety of passengers on unmanned vehicles. The collision avoidance system can select a predicted avoidance action to generate a minimum amount of acceleration in the passenger compartment of the unmanned vehicle.

The unmanned driving vehicle may further include a wireless communication system that allows communication between an unmanned driving vehicle and an external system such as equipment, sensors, other vehicles and / or control devices, servers, etc. in its surrounding environment, Information about the environment surrounding the vehicle such as weather information, and so on. For example, a wireless communication system may communicate directly with one or more devices or wirelessly via a communication network. The wireless communication system may utilize any cellular network or a wireless local area network (WLAN) and may utilize, for example, WiFi. A wireless communication system may communicate directly with the device using, for example, an infrared link, Bluetooth, or the like.

It should be noted that some or all of the members as described above may be implemented in software, hardware, or a combination thereof. For example, such a member may be implemented in software installed and stored on a persistent storage device, which is loaded into memory by a processor (not shown) and executed to perform the described process or operation throughout this application . Alternatively, such elements may be implemented as executable code programmed or embedded in dedicated hardware, such as an integrated circuit (e.g., an application specific integrated circuit or ASIC), a digital signal processor (DSP), or a field programmable gate array And the dedicated hardware can be accessed from the application via the corresponding driver and / or operating system. Such a member may also be implemented as part of a set of instructions that can be read by the software component via one or more specific instructions, or as specific hardware logic in the processor or processor core.

Figure 8 is a block diagram illustrating an example of a data processing system that may be used in conjunction with one embodiment of the present invention. For example, the system 1500 may represent any data processing system that performs any one of the above-described processes or methods, for example, the data processing system 110 or server 102 of FIG. 1, . The system 1500 may include many different elements. Such members may be integrated into a computer system's chassis (e.g., a computer system), an integrated circuit (IC), a portion of an integrated circuit, discrete electronics or other modules suitable for a circuit board such as a motherboard or add- chassis of the present invention.

It should be noted that the system 1500 is intended to illustrate the drawings from a macro perspective of many components of a computer system. It should be understood, however, that there may be increased members in some embodiments, and, in other embodiments, there may be different arrangements of members described. The system 1500 can be a desktop computer, a laptop computer, a tablet computer, a server, a mobile phone, a media player, a personal digital assistant (PDA), a smart watch, a personal mobile communication device, a gaming device, a network router or hub, AP) or a repeater, set-top box, or a combination thereof. Also, while a single machine or system is shown, the term " machine " or " system " means that a set of (or multiple sets of) instructions may be executed alone or in combination to perform any one or more of the methods described herein Or any combination of < RTI ID = 0.0 > machines / systems < / RTI >

In one embodiment, the system 1500 includes a processor 1501, a memory 1503, and devices 1505-1508 coupled through a bus line or interconnect 1510. The processor 1501 may represent a single processor or multiple processors including a single processor core or multiple process cores. The processor 1501 may represent one or more general purpose processors, such as a microprocessor, a central processing unit (CPU), and the like. In more detail, processor 1501 may be a processor that implements a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a long instruction word (VLIW) microprocessor, May be a processor that implements the combination. Processor 1501 may be an application specific integrated circuit (ASIC), a cellular or baseband processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, a graphics processor, , A built-in processor, or any other type of logic capable of processing instructions.

The processor 1501 may serve as a main processing unit and central hub for communicating with various members of the system, and the processor 1501 may be a low-power multi-core processor socket, such as an ultra-low voltage processor. Such a processor may be implemented as a system on chip (SoC). The processor 1501 is configured to execute instructions for performing the operations and steps described herein. The system 1500 may further include a graphical interface for communicating with the optional graphics subsystem 1504 and the graphics subsystem 1504 may include a display control device, a graphics processor, and / or a display device .

The processor 1501 may communicate with the memory 1503, which in one embodiment may be implemented in multiple memory devices and may provide a nominal amount of system memory. Memory 1503 may include one or more volatile storage (or memory) devices or other types of storage devices such as random access memory (RAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), static RAM can do. Memory 1503 may store information including instruction sequences that are executed by processor 1501 or any other device. For example, executable code and / or data of various operating systems, device drivers, firmware (e.g., basic input / output system or BIOS) and / or application programs may be loaded into memory 1503, Lt; / RTI > The operating system can be any type of operating system, such as the Microsoft® Windows® operating system, Apple's Mac OS® / iOS®, Google®'s Android®, Linux®, Unix®, or VxWorks Other real-time or embedded operating system.

The system 1500 may further include an IO device, such as devices 1505-1508, including a network interface device 1505, an optional input device 1506, and other optional IO devices 1507. The network interface device 1505 may include a wireless transceiver and / or a network interface card (NIC). The wireless transceiver may be a WiFi transceiver, an infrared transceiver, a Bluetooth transceiver, a WiMax transceiver, a wireless cellular telephony transceiver, a satellite transceiver (e.g., a global positioning system (GPS) transceiver) or other radio frequency (RF) transceiver, . The NIC may be an Ethernet card.

The input device 1506 may be a mouse, a touchpad, a touch sensitive screen (which may be incorporated into the display device 1504), an indicating device such as a stylus, and / or a keyboard (e.g., As shown in FIG. For example, the input device 1506 may include a touch screen control device coupled to the touch screen. The touch screen and touch screen control devices utilize, for example, any one of a number of touch sensitive techniques and surface acoustic wave techniques and other proximity angular sensor arrays or other elements for determining one or more contact points with the touch screen To detect contact and movement or interruption of the touch screen.

IO device 1507 may include an audio device. The audio device may include speakers and / or microphones to enable voice support functions such as speech recognition, voice replication, digital recording and / or telephony functions. The other IO devices 1507 may be a general purpose serial bus line (USB) port, a parallel port, a serial port, a printer, a network interface, a bus line bridge (PCI-PCI bridge), a sensor (e.g., an accelerometer, a gyroscope, , A magnetometer, an optical sensor, a compass, a proximity sensor, or the like), or a combination thereof. The device 1507 may further include an imaging processing subsystem (e.g., a camera), which may be a photo sensor, such as a solid state image sensor (CCD) or a complementary metal oxide semiconductor (CMOS) And an optical sensor to enable camera functions such as recording video clips. Some sensors may be connected to the interconnect 1510 via a sensor hub (not shown), and other devices such as a keyboard or temperature sensor may be controlled by an embedded control device (not shown) And is determined by a specific configuration or design.

A mass storage device (not shown) may be coupled to the processor 1501 to provide persistent storage for data, applications, one or more operating systems, and the like. In various embodiments, the mass storage device may be implemented via a solid state device (SSD) to achieve thinner and lighter system designs and improve system responsiveness. However, in other embodiments, the mass storage device may be implemented primarily using a hard disk drive (HDD), which allows a relatively small amount of SSD storage to act as an SSD cache, By realizing non-volatile storage of information, fast power-up can be realized when system activity is resumed. In addition, the flash device may be coupled to the processor 1501 via, for example, a serial peripheral device interface (SPI). Such a flash device may provide nonvolatile storage of system software including basic input / output software (BIOS) and other firmware of the system.

Storage device 1508 may include one or more sets of instructions or software (e.g., modules, units and / or logic 1528) that implement any one or more of the methods or functions described herein Readable storage medium 1509 (also referred to as a machine-readable storage medium or computer readable medium). Module / unit / logic 1528 may represent any one of the aforementioned members, such as, for example, the search engine, coder, alternate log recording module described above. Module / unit / logic 1528 may be located entirely or at least partially within memory 1503 and / or within processor 1501 while being executed by data processing system 1500, wherein memory 1503 and processor (s) 1501 constitute a machine-readable storage medium. Module / unit / logic 1528 may further be sent or received over the network via network interface device 1505.

The computer readable storage medium 1509 may be for permanently storing some of the software functions described above. Although the computer readable storage medium 1509 in the exemplary embodiment is shown as a single medium, the term " computer-readable storage medium " refers to a medium or medium that stores one or more sets of instructions, , A centralized or distributed database, and / or an associated cache and server). In addition, the term " computer-readable storage medium " encompasses any medium that can be stored on or encoded by a set of instructions that is executed by a machine and causes the machine to perform one or more of the methods of the present invention I have to understand. Thus, it should be understood that the term " computer readable storage medium " includes, but is not limited to, solid state memory and optical and magnetic media or any other non-volatile machine-readable media.

The module / unit / logic 1528, members, and other features described herein may be implemented with discrete hardware components or integrated into the functionality of hardware components such as ASICS, FPGAs, DSPs, or similar devices. In addition, the module / unit / logic 1528 may be implemented as firmware or functional circuitry within a hardware device. Module / unit / logic 1528 may also be implemented in any combination of hardware devices and software components.

The system 1500 is illustrated with various components of a data processing system, but these details are not closely related to the embodiments of the present invention, so any representative system structures or representations of interconnections of members . It will also be appreciated that other data processing systems that may include a network computer, a portable computer, a mobile phone, a server, and / or fewer or more members in an embodiment of the present invention may also be used.

Algorithms and symbolic representations of operations on data bits already in computer memory have indicated some of these details. These algorithmic descriptions and representations are the manner in which those skilled in the data processing arts use them to efficiently convey the gist of their work to others skilled in the art. Here, an algorithm is typically envisioned as a self-consistent sequence of operations to achieve the desired result. These operations are operations that require physical manipulation of physical quantities.

It should be noted, however, that all such terms and similar terms are to be associated with the appropriate physical quantity and are merely convenient labels applied to such quantities. As will be apparent from the foregoing description, unless explicitly stated otherwise, it is to be understood that the description made using the terms recited in the appended claims refers to the operations and processes of a computer system or similar electronic computing device throughout the specification. , Which computer system or similar electronic computing device can manipulate data represented by physical (electronic) quantities in the registers and memories of the computer system and to store the data in computer system memory or registers or other similar information storage, To other data similarly expressed in physical quantities.

Embodiments of the invention relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a format that can be read by a machine (e.g., a computer). For example, a machine-readable (e.g., computer readable) medium can be a machine-readable storage medium (e.g., read only memory ("ROM"), random access memory Quot;), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods illustrated in the accompanying drawings may be implemented with processing logic including hardware (e.g., circuitry, dedicated logic, etc.), software (e.g., embedded in a non-volatile computer readable medium) Lt; / RTI > Although the foregoing process or method has been described above by some sequential operation, it will be appreciated that some of the described operations may be performed in a different order. Also, some operations may be performed in parallel, not in a sequential order.

Embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that various programming languages may be used to implement the teachings of the embodiments of the invention described herein.

In the foregoing specification, embodiments of the present invention have been described with reference to the detailed exemplary embodiments of the present invention. It will be apparent that various changes may be made therein without departing from the spirit and scope of the invention as set forth in the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Claims (26)

Transmitting a vehicle identifier (ID) and vehicle information of a first unmanned driving vehicle to a cloud server communicably connected to a plurality of unmanned driving vehicles through a network;
Receiving, from the cloud server, a first list of one or more vehicle identifiers (IDs) that recognize one or more unattended operating vehicles within a predetermined geographical proximity of the first unattended vehicle; And
A second unmanned driving vehicle in a first list selected from the one or more unmanned driving vehicles via a wireless local area network (WLAN) so that the first unmanned driving vehicle and the second unmanned driving vehicle exchange operating states; A computer-implemented method for inter-vehicle communication between an unmanned driving vehicle comprising: The method according to claim 1,
Wherein the unattended operation vehicle in the first list is recognized by the cloud server based on the position information of the first unmanned operation vehicle and the position information of one or more unmanned operation vehicles. A computer realization method for communication between vehicles. 3. The method of claim 2,
Wherein the cloud server maintains a neighboring vehicle data structure storing geographical neighbor information of the plurality of unmanned driving vehicles based on the respective vehicle information received periodically from the plurality of unmanned driving vehicles A computer-implemented method for inter-vehicle communication between unmanned driving vehicles. The method of claim 3,
Wherein the k-dimensional (kd) tree comprises a plurality of nodes representing a hierarchy, each node comprising a plurality of nodes managed by the cloud server, wherein the k- Of the unmanned operation vehicle (1). The method according to claim 1,
Wherein each vehicle ID in the first list includes an Internet Protocol (IP) address and a domain associated with the corresponding unattended operating vehicle to communicate via the wireless local area network. Computer realization method for communication. The method according to claim 1,
Wherein the operating condition includes an indicator for instructing the first unmanned operating vehicle to change from a first lane currently occupied by the first unmanned operating vehicle to a second lane occupied by the second unmanned operating vehicle Wherein the vehicle-to-vehicle communication between the unmanned driving vehicles is characterized by the following. The method according to claim 1,
Wherein the second unmanned driving vehicle periodically updates vehicle information using the cloud server, and the second unmanned driving vehicle receives from the cloud server a request for updating the vehicle information of one or more nearby unmanned driving vehicles including the first unmanned driving vehicle 2 < / RTI > list is received. 17. A non-transitory machine-readable medium having instructions stored thereon, the instructions, when executed by a processor,
(ID) of the first unmanned driving vehicle and the vehicle information to the cloud server communicably connected to the plurality of unmanned driving vehicles through the network,
Receiving, from the cloud server, a first list of one or more vehicle identifiers (IDs) that recognize one or more unattended operating vehicles within a predetermined geographical proximity of the first unattended vehicle,
Communicating with a second unattended operating vehicle in a first list selected from the one or more unattended operating vehicles via a wireless local area network such that the first unattended operating vehicle and the second unattended operating vehicle exchange operational status To perform an operation for vehicle-to-vehicle communication comprising the non-transitory machine-readable medium. 9. The method of claim 8,
Characterized in that the unmanned operation vehicle in the first list is recognized by the cloud server based on the position information of the first unmanned operation vehicle and the vehicle information of the one or more unmanned operation vehicles. media. 10. The method of claim 9,
Wherein the cloud server maintains an adjacent vehicle data structure storing geographical neighbor information of the plurality of unmanned driving vehicles based on the respective vehicle information received periodically from the plurality of unmanned driving vehicles. Machine readable medium. 11. The method of claim 10,
Wherein the k-dimensional (kd) tree comprises a plurality of nodes representing a hierarchy, each node comprising a plurality of nodes managed by the cloud server, wherein the k- ≪ / RTI > of the vehicle. 9. The method of claim 8,
Wherein each vehicle ID in the first list includes an Internet Protocol (IP) address and domain associated with a corresponding unattended operating vehicle to communicate via the wireless local area network. 9. The method of claim 8,
Wherein the operating state includes an indicator for instructing the first unmanner driving vehicle to change from a first lane occupied by the first unmanned driving vehicle to a second lane occupied by the second unmanned driving vehicle Non-transitory machine-readable medium. 9. The method of claim 8,
Wherein the second unmanned driving vehicle periodically updates its vehicle information using the cloud server, and the second unmanned driving vehicle is one of the one or more nearby unmanned driving vehicles including the first unmanned driving vehicle from the cloud server ≪ / RTI > wherein the second list is received. A data processing system operating in an unmanned on-board vehicle,
A processor; And
A memory coupled to the processor and storing instructions,
Wherein the instructions, when executed by the processor, cause the processor to:
(ID) of the first unmanned driving vehicle and the vehicle information to the cloud server communicably connected to the plurality of unmanned driving vehicles through the network,
Receiving, from the cloud server, a first list of one or more vehicle identifiers (IDs) that recognize one or more unattended operating vehicles within a predetermined geographical proximity of the first unattended vehicle,
Communicating with a second unattended operating vehicle of a first list selected from the one or more unattended operating vehicles via a wireless local area network to exchange operational statuses of the first unattended operating vehicle and the second unattended operating vehicle And to execute the operation including the data. 16. The method of claim 15,
Wherein the unattended operation vehicle in the first list is recognized by the cloud server based on the position information of the first unmanned operation vehicle and the vehicle information of one or more unmanned operation vehicles. 17. The method of claim 16,
Wherein the cloud server maintains an adjacent vehicle data structure storing geographical neighbor information of the plurality of unmanned driving vehicles based on the respective vehicle information received from the plurality of unmanned operation vehicles periodically. system. 18. The method of claim 17,
Wherein the k-dimensional (kd) tree comprises a plurality of nodes, wherein each node comprises a plurality of unattended operating vehicles managed by the cloud server, Of the data processing system. 16. The method of claim 15,
Wherein each vehicle ID in the list comprises an internet protocol (IP) address and a domain associated with the corresponding unattended operating vehicle to communicate via the wireless local area network. 16. The method of claim 15,
Wherein the operating state includes an indicator for instructing the first unmanner driving vehicle to change from a first lane occupied by the first unmanned driving vehicle to a second lane occupied by the second unmanned driving vehicle Data processing system. 16. The method of claim 15,
Wherein the second unmanned driving vehicle periodically updates its vehicle information using the cloud server, and the second unmanned driving vehicle is one of the one or more nearby unmanned driving vehicles including the first unmanned driving vehicle from the cloud server And the second list is received. A computer-implemented method for managing a state of an unmanned driving vehicle,
Maintaining, in a cloud server, an adjacent vehicle data structure storing vehicle metadata of a plurality of unattended operating vehicles communicatively coupled to the cloud server via a network;
Receiving a first update request including a first vehicle identifier (ID) of the first unmanned driving vehicle and current vehicle metadata from a first driving vehicle of the unmanned driving vehicle;
In response to the first update request, update the neighboring vehicle data structure to update the first node corresponding to the first unmanned driving vehicle based on the first vehicle ID of the first unmanned driving vehicle and the current vehicle metadata Traversing (tr un-driving vehicle ersing); And
Wherein the first unmanned operating vehicle is operatively connected to the first unmanned operating vehicle through one or more of the plurality of predetermined unauthorized operating vehicles through the network to allow the first unmanned operating vehicle to communicate directly with any unattended operating vehicle on the list via a wireless local area network And transmitting the list of one or more vehicle IDs recognizing the unmanned driving vehicle to the first unmanned driving vehicle. 23. The method of claim 22,
Wherein the adjacent vehicle data structure includes a plurality of nodes in a hierarchical structure and each node stores vehicle metadata of the corresponding unattended operating vehicle in correspondence with one of the plurality of unattended operating vehicles. A computer-implemented method for managing the status of a driving vehicle. 23. The method of claim 22,
Wherein the adjacent vehicle data structure comprises a k-dimensional (kd) tree. 24. The method of claim 23,
Wherein the vehicle metadata of the unmanned driving vehicle includes at least one of a current position, a speed and a moving direction of the unmanned driving vehicle. 23. The method of claim 22,
Wherein the vehicle ID includes an Internet Protocol (IP) address corresponding to the unmanned driving vehicle.

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